Triple
T4785270
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LPPT |
E106459
|
entity |
| Predicate | hasPassengerTrafficRankInPortugal |
P25678
|
FINISHED |
| Object | 1 |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: 1 | Statement: [LPPT, hasPassengerTrafficRankInPortugal, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPassengerTrafficRankInPortugal Context triple: [LPPT, hasPassengerTrafficRankInPortugal, 1]
-
A.
populationRankInPortugal
Indicates the relative position of an entity in terms of population size compared to other entities within Portugal.
-
B.
hasPassengerTrafficRank
chosen
Indicates the relative position or ranking of an entity based on the volume of passenger traffic it handles compared to others.
-
C.
passengerTrafficRankInEurope
Indicates the relative position of an entity in Europe based on the volume of passenger traffic it handles.
-
D.
hasPassengerTrafficRankInLatinAmerica
Indicates the relative position of an entity in terms of passenger traffic volume compared to other entities within Latin America.
-
E.
cargoTrafficRankInEurope
Indicates the relative position of an entity in terms of cargo traffic volume compared to other entities within Europe.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69bd43f4a9588190bf73e20bc27c03cc |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd65b0ea408190b348d917883381de |
completed | March 20, 2026, 3:20 p.m. |
| PD | Predicate disambiguation | batch_69bd622e1b408190806c15c61519fc74 |
completed | March 20, 2026, 3:05 p.m. |
Created at: March 20, 2026, 1:22 p.m.